EP. 107 - I Test AI Products For Before Writing Code (Save Thousands In Tokens) UX For AI podcast

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EP. 107 - I Test AI Products for Before Writing Code (Save Thousands in Tokens)

2d ago 11:37

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You’re probably burning tokens on an AI feature your users don’t want.

You’re not alone. Lots of teams are forcing “AI-native” into products.

Here’s the good news: Lean Startup still applies to AI. You can validate the experience without writing a single line of app code.

👉 The No-Code AI MVP (my playbook)

Goal: simulate your production AI service in a controlled “cloud project” so you can iterate.

1. Define outcomes, not features. What must the AI accomplish that a user would pay for?

2. Write the system brief. Tone, boundaries, escalation rules, guardrails.

3. Seed context (“memory”). Drop your internal docs.

4. Mimic RAG. Keep a simple database (call it Memory) and paste each full conversation into it. Force the agent to read Memory first on the next turn.

5. Design success criteria you can score.

6. Operate it yourself. Founder + team use it daily. Track token spend, failure reasons, retrieval misses.

7. Tight feedback loop. Adjust instructions and Memory structure. Re-run the same tasks. (don't forget: create a log of all the changes so you can retrace your steps)

8. Decide. When quality and cost are predictable, then (and only then) think UI, auth, billing.

Cost is a product decision! Your token diet can make or break the business. Bottom line: Build the AI service right before you build around it.

P.S. If you’re building an AI-led product, DM me “PROTOTYPE.” I’ll walk you through the exact worksheet and Memory schema we used in this video.

#Prototyping #AI #AInative #RAG #MVP #ux

Interested in joining the podcast? DM Behrad on LinkedIn:
https://www.linkedin.com/in/behradmirafshar/
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